Overview

Dataset statistics

Number of variables11
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory91.6 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical2

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
400kmDensity is highly overall correlated with SYM/H_INDEX_nT and 5 other fieldsHigh correlation
SYM/H_INDEX_nT is highly overall correlated with 400kmDensityHigh correlation
1-M_AE_nT is highly overall correlated with SYM/H_INDEX_nTHigh correlation
DAILY_SUNSPOT_NO_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
DAILY_F10.7_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
SOLAR_LYMAN-ALPHA_W/m^2 is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
mg_index (core to wing ratio (unitless)) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
irradiance (W/m^2/nm) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
storm is highly overall correlated with storm phaseHigh correlation
storm phase is highly overall correlated with stormHigh correlation
storm is uniformly distributedUniform
SYM/H_INDEX_nT has 28151 (2.8%) zerosZeros
DAILY_SUNSPOT_NO_ has 249842 (25.0%) zerosZeros
d_diff has 15134 (1.5%) zerosZeros

Reproduction

Analysis started2023-02-24 23:59:45.962966
Analysis finished2023-02-25 00:00:39.270390
Duration53.31 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

400kmDensity
Real number (ℝ)

Distinct945437
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4484876 × 10-12
Minimum4.683731 × 10-16
Maximum2.4308 × 10-11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T19:00:40.412381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4.683731 × 10-16
5-th percentile1.9833396 × 10-13
Q14.8059028 × 10-13
median9.31384 × 10-13
Q31.8720472 × 10-12
95-th percentile4.5452104 × 10-12
Maximum2.4308 × 10-11
Range2.4307532 × 10-11
Interquartile range (IQR)1.391457 × 10-12

Descriptive statistics

Standard deviation1.4531059 × 10-12
Coefficient of variation (CV)1.0031883
Kurtosis0
Mean1.4484876 × 10-12
Median Absolute Deviation (MAD)5.5750415 × 10-13
Skewness0
Sum1.4484876 × 10-6
Variance2.1115168 × 10-24
MonotonicityNot monotonic
2023-02-24T19:00:40.542007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.253446 × 10-125
 
< 0.1%
2.023682 × 10-125
 
< 0.1%
1.008295 × 10-125
 
< 0.1%
1.371403 × 10-124
 
< 0.1%
1.096027 × 10-124
 
< 0.1%
1.13699 × 10-124
 
< 0.1%
1.141231 × 10-124
 
< 0.1%
1.223202 × 10-124
 
< 0.1%
1.036332 × 10-124
 
< 0.1%
1.012206 × 10-124
 
< 0.1%
Other values (945427) 999957
> 99.9%
ValueCountFrequency (%)
4.683731 × 10-161
< 0.1%
4.738029 × 10-161
< 0.1%
8.853198 × 10-161
< 0.1%
1.005823 × 10-151
< 0.1%
1.211596 × 10-151
< 0.1%
1.367567 × 10-151
< 0.1%
1.420044 × 10-151
< 0.1%
1.548175 × 10-151
< 0.1%
1.739733 × 10-151
< 0.1%
1.756266 × 10-151
< 0.1%
ValueCountFrequency (%)
2.4308 × 10-111
< 0.1%
2.303079 × 10-111
< 0.1%
2.122304 × 10-111
< 0.1%
2.094839 × 10-111
< 0.1%
2.056448 × 10-111
< 0.1%
2.044702 × 10-111
< 0.1%
1.949333 × 10-111
< 0.1%
1.927607 × 10-111
< 0.1%
1.909414 × 10-111
< 0.1%
1.87444 × 10-111
< 0.1%

SYM/H_INDEX_nT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct515
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.740491
Minimum-487
Maximum143
Zeros28151
Zeros (%)2.8%
Negative756961
Negative (%)75.7%
Memory size15.3 MiB
2023-02-24T19:00:40.668700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-487
5-th percentile-40
Q1-17
median-8
Q3-1
95-th percentile9
Maximum143
Range630
Interquartile range (IQR)16

Descriptive statistics

Standard deviation18.423993
Coefficient of variation (CV)-1.7153772
Kurtosis54.585679
Mean-10.740491
Median Absolute Deviation (MAD)8
Skewness-4.2889191
Sum-10740491
Variance339.44354
MonotonicityNot monotonic
2023-02-24T19:00:40.783395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3 37683
 
3.8%
-4 36641
 
3.7%
-2 36276
 
3.6%
-5 35874
 
3.6%
-7 35249
 
3.5%
-6 34858
 
3.5%
-1 34194
 
3.4%
-8 33897
 
3.4%
-9 32893
 
3.3%
-10 30821
 
3.1%
Other values (505) 651614
65.2%
ValueCountFrequency (%)
-487 1
< 0.1%
-483 1
< 0.1%
-481 1
< 0.1%
-476 1
< 0.1%
-470 1
< 0.1%
-469 1
< 0.1%
-465 2
< 0.1%
-461 1
< 0.1%
-455 2
< 0.1%
-452 1
< 0.1%
ValueCountFrequency (%)
143 1
 
< 0.1%
128 1
 
< 0.1%
127 1
 
< 0.1%
124 1
 
< 0.1%
122 1
 
< 0.1%
121 1
 
< 0.1%
115 1
 
< 0.1%
112 3
< 0.1%
110 1
 
< 0.1%
109 1
 
< 0.1%

1-M_AE_nT
Real number (ℝ)

Distinct2023
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.02039
Minimum1
Maximum4174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T19:00:40.914043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q137
median82
Q3223
95-th percentile602
Maximum4174
Range4173
Interquartile range (IQR)186

Descriptive statistics

Standard deviation208.21612
Coefficient of variation (CV)1.2318994
Kurtosis9.7044017
Mean169.02039
Median Absolute Deviation (MAD)57
Skewness2.530692
Sum1.6902039 × 108
Variance43353.954
MonotonicityNot monotonic
2023-02-24T19:00:41.034718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 9565
 
1.0%
27 9373
 
0.9%
30 9349
 
0.9%
33 9303
 
0.9%
31 9262
 
0.9%
35 9191
 
0.9%
29 9176
 
0.9%
28 9176
 
0.9%
34 9163
 
0.9%
36 9136
 
0.9%
Other values (2013) 907306
90.7%
ValueCountFrequency (%)
1 26
 
< 0.1%
2 173
 
< 0.1%
3 630
 
0.1%
4 1276
 
0.1%
5 2070
 
0.2%
6 2961
0.3%
7 3671
0.4%
8 4398
0.4%
9 5029
0.5%
10 5636
0.6%
ValueCountFrequency (%)
4174 1
< 0.1%
3583 1
< 0.1%
3549 1
< 0.1%
3540 1
< 0.1%
3437 1
< 0.1%
3422 1
< 0.1%
3415 1
< 0.1%
3413 1
< 0.1%
3353 1
< 0.1%
3217 1
< 0.1%

DAILY_SUNSPOT_NO_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct214
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.338448
Minimum0
Maximum281
Zeros249842
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T19:00:41.153400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median30
Q371
95-th percentile148
Maximum281
Range281
Interquartile range (IQR)66

Descriptive statistics

Standard deviation49.952263
Coefficient of variation (CV)1.0779874
Kurtosis1.4869828
Mean46.338448
Median Absolute Deviation (MAD)30
Skewness1.3224454
Sum46338448
Variance2495.2286
MonotonicityNot monotonic
2023-02-24T19:00:41.267069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 249842
25.0%
12 24673
 
2.5%
13 24244
 
2.4%
15 19578
 
2.0%
14 19161
 
1.9%
18 14520
 
1.5%
26 13626
 
1.4%
16 13575
 
1.4%
11 11936
 
1.2%
17 9989
 
1.0%
Other values (204) 598856
59.9%
ValueCountFrequency (%)
0 249842
25.0%
5 704
 
0.1%
6 1544
 
0.2%
7 3742
 
0.4%
8 3049
 
0.3%
9 4991
 
0.5%
10 8283
 
0.8%
11 11936
 
1.2%
12 24673
 
2.5%
13 24244
 
2.4%
ValueCountFrequency (%)
281 279
< 0.1%
279 279
< 0.1%
270 291
< 0.1%
267 256
< 0.1%
263 256
< 0.1%
252 274
< 0.1%
250 518
0.1%
248 574
0.1%
247 541
0.1%
239 275
< 0.1%

DAILY_F10.7_
Real number (ℝ)

Distinct927
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.671341
Minimum65.1
Maximum999.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T19:00:41.388772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum65.1
5-th percentile67.5
Q171.2
median84.2
Q3109.9
95-th percentile157.4
Maximum999.9
Range934.8
Interquartile range (IQR)38.7

Descriptive statistics

Standard deviation52.488133
Coefficient of variation (CV)0.54295443
Kurtosis195.02813
Mean96.671341
Median Absolute Deviation (MAD)14.7
Skewness11.744705
Sum96671341
Variance2755.0041
MonotonicityNot monotonic
2023-02-24T19:00:41.506456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.3 8076
 
0.8%
68 7884
 
0.8%
69.5 7430
 
0.7%
69.8 7416
 
0.7%
68.8 7232
 
0.7%
67.4 6651
 
0.7%
67.8 6502
 
0.7%
70.3 6499
 
0.6%
69.1 6453
 
0.6%
69.6 6379
 
0.6%
Other values (917) 929478
92.9%
ValueCountFrequency (%)
65.1 265
 
< 0.1%
65.2 360
 
< 0.1%
65.5 346
 
< 0.1%
65.6 320
 
< 0.1%
65.8 684
 
0.1%
65.9 611
 
0.1%
66 1056
0.1%
66.1 1033
0.1%
66.2 2538
0.3%
66.3 2240
0.2%
ValueCountFrequency (%)
999.9 2251
0.2%
275.4 266
 
< 0.1%
270.9 267
 
< 0.1%
267.6 252
 
< 0.1%
254 354
 
< 0.1%
246.9 291
 
< 0.1%
245.2 275
 
< 0.1%
242.6 281
 
< 0.1%
240.6 336
 
< 0.1%
232.8 285
 
< 0.1%

SOLAR_LYMAN-ALPHA_W/m^2
Real number (ℝ)

Distinct1687
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0068046211
Minimum0.00588
Maximum0.009751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T19:00:41.634089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00588
5-th percentile0.005982
Q10.006154
median0.006554
Q30.007295
95-th percentile0.008303
Maximum0.009751
Range0.003871
Interquartile range (IQR)0.001141

Descriptive statistics

Standard deviation0.00077343999
Coefficient of variation (CV)0.11366393
Kurtosis0.47633234
Mean0.0068046211
Median Absolute Deviation (MAD)0.0005
Skewness1.0203737
Sum6804.6211
Variance5.9820942 × 10-7
MonotonicityNot monotonic
2023-02-24T19:00:41.762772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00602 3330
 
0.3%
0.006027 3217
 
0.3%
0.005978 2952
 
0.3%
0.006047 2933
 
0.3%
0.005991 2823
 
0.3%
0.006006 2794
 
0.3%
0.006071 2641
 
0.3%
0.006 2565
 
0.3%
0.006013 2552
 
0.3%
0.005999 2545
 
0.3%
Other values (1677) 971648
97.2%
ValueCountFrequency (%)
0.00588 355
 
< 0.1%
0.005897 343
 
< 0.1%
0.005898 314
 
< 0.1%
0.005904 354
 
< 0.1%
0.005907 692
0.1%
0.005908 290
 
< 0.1%
0.005909 350
 
< 0.1%
0.00591 1125
0.1%
0.005912 271
 
< 0.1%
0.005913 351
 
< 0.1%
ValueCountFrequency (%)
0.009751 285
< 0.1%
0.00974 281
< 0.1%
0.00972 274
< 0.1%
0.009662 291
< 0.1%
0.009581 245
< 0.1%
0.009577 279
< 0.1%
0.009555 335
< 0.1%
0.00954 349
< 0.1%
0.009511 360
< 0.1%
0.009483 256
< 0.1%
Distinct2312
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26847511
Minimum0.26295999
Maximum0.28494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T19:00:41.904392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.26295999
5-th percentile0.26372999
Q10.2646745
median0.26694756
Q30.27134403
95-th percentile0.27767748
Maximum0.28494
Range0.02198001
Interquartile range (IQR)0.00666953

Descriptive statistics

Standard deviation0.0045568954
Coefficient of variation (CV)0.016973251
Kurtosis0.21803932
Mean0.26847511
Median Absolute Deviation (MAD)0.00263756
Skewness1.0059587
Sum268475.11
Variance2.0765296 × 10-5
MonotonicityNot monotonic
2023-02-24T19:00:42.036041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26708001 2652
 
0.3%
0.26403001 2432
 
0.2%
0.26418999 2343
 
0.2%
0.26532999 2317
 
0.2%
0.26469001 2214
 
0.2%
0.26475999 2166
 
0.2%
0.26370001 1940
 
0.2%
0.26559001 1887
 
0.2%
0.26697999 1869
 
0.2%
0.26363 1859
 
0.2%
Other values (2302) 978321
97.8%
ValueCountFrequency (%)
0.26295999 337
< 0.1%
0.26299 323
< 0.1%
0.26300001 348
< 0.1%
0.26304999 351
< 0.1%
0.26306999 341
< 0.1%
0.26308 360
< 0.1%
0.26309001 361
< 0.1%
0.26311001 354
< 0.1%
0.26312 280
< 0.1%
0.26313001 633
0.1%
ValueCountFrequency (%)
0.28494 281
< 0.1%
0.28485999 285
< 0.1%
0.28428999 291
< 0.1%
0.28426999 274
< 0.1%
0.2841 279
< 0.1%
0.28386 335
< 0.1%
0.28376999 349
< 0.1%
0.28373272 361
< 0.1%
0.28373 313
< 0.1%
0.28360999 258
< 0.1%

irradiance (W/m^2/nm)
Real number (ℝ)

Distinct3232
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0055002052
Minimum0.0048730583
Maximum0.0073493496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T19:00:42.161705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0048730583
5-th percentile0.0049224854
Q10.0050362838
median0.0053156056
Q30.0058406205
95-th percentile0.0066085062
Maximum0.0073493496
Range0.0024762913
Interquartile range (IQR)0.00080433674

Descriptive statistics

Standard deviation0.00054255913
Coefficient of variation (CV)0.098643436
Kurtosis0.21875877
Mean0.0055002052
Median Absolute Deviation (MAD)0.00035282597
Skewness0.96712922
Sum5500.2052
Variance2.9437041 × 10-7
MonotonicityNot monotonic
2023-02-24T19:00:42.288366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00495163817 1040
 
0.1%
0.004914534744 1037
 
0.1%
0.00491212029 975
 
0.1%
0.004943745211 753
 
0.1%
0.00519876834 746
 
0.1%
0.004931729753 744
 
0.1%
0.004945786204 739
 
0.1%
0.004933540244 733
 
0.1%
0.005009988789 729
 
0.1%
0.004992022645 721
 
0.1%
Other values (3222) 991783
99.2%
ValueCountFrequency (%)
0.004873058293 368
< 0.1%
0.004877128173 344
< 0.1%
0.004877185915 367
< 0.1%
0.004877588246 349
< 0.1%
0.004881324712 127
 
< 0.1%
0.004881698173 204
< 0.1%
0.004881755915 341
< 0.1%
0.00488556223 259
< 0.1%
0.004885710776 377
< 0.1%
0.004885739647 372
< 0.1%
ValueCountFrequency (%)
0.007349349558 268
< 0.1%
0.00734248152 291
< 0.1%
0.007334709167 251
< 0.1%
0.007301890757 278
< 0.1%
0.007268224377 359
< 0.1%
0.007266042288 276
< 0.1%
0.007259562146 333
< 0.1%
0.007257604506 309
< 0.1%
0.007247306872 324
< 0.1%
0.007218547165 342
< 0.1%

storm
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
1
500000 
-1
500000 

Length

Max length2
Median length1.5
Mean length1.5
Min length1

Characters and Unicode

Total characters1500000
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 500000
50.0%
-1 500000
50.0%

Length

2023-02-24T19:00:42.399044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T19:00:42.495812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1000000
66.7%
- 500000
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
66.7%
Dash Punctuation 500000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1000000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 500000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1500000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1000000
66.7%
- 500000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1000000
66.7%
- 500000
33.3%

storm phase
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
-1
500000 
2
302266 
1
197734 

Length

Max length2
Median length1.5
Mean length1.5
Min length1

Characters and Unicode

Total characters1500000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
-1 500000
50.0%
2 302266
30.2%
1 197734
 
19.8%

Length

2023-02-24T19:00:42.577564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T19:00:42.682286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 697734
69.8%
2 302266
30.2%

Most occurring characters

ValueCountFrequency (%)
1 697734
46.5%
- 500000
33.3%
2 302266
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000000
66.7%
Dash Punctuation 500000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 697734
69.8%
2 302266
30.2%
Dash Punctuation
ValueCountFrequency (%)
- 500000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1500000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 697734
46.5%
- 500000
33.3%
2 302266
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 697734
46.5%
- 500000
33.3%
2 302266
20.2%

d_diff
Real number (ℝ)

Distinct836060
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6751977 × 10-16
Minimum-7.241425 × 10-12
Maximum8.884224 × 10-12
Zeros15134
Zeros (%)1.5%
Negative480932
Negative (%)48.1%
Memory size15.3 MiB
2023-02-24T19:00:42.788030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-7.241425 × 10-12
5-th percentile-1.599511 × 10-13
Q1-3.5324325 × 10-14
median4.572 × 10-16
Q33.70282 × 10-14
95-th percentile1.5546815 × 10-13
Maximum8.884224 × 10-12
Range1.6125649 × 10-11
Interquartile range (IQR)7.2352525 × 10-14

Descriptive statistics

Standard deviation1.6287274 × 10-13
Coefficient of variation (CV)972.25986
Kurtosis0
Mean1.6751977 × 10-16
Median Absolute Deviation (MAD)3.618725 × 10-14
Skewness0
Sum1.6751977 × 10-10
Variance2.6527531 × 10-26
MonotonicityNot monotonic
2023-02-24T19:00:42.912698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15134
 
1.5%
-2.798 × 10-1511
 
< 0.1%
4.446 × 10-1410
 
< 0.1%
-3.7364 × 10-149
 
< 0.1%
-5.95 × 10-159
 
< 0.1%
2.4641 × 10-149
 
< 0.1%
-1.3778 × 10-149
 
< 0.1%
1.1588 × 10-149
 
< 0.1%
1.0818 × 10-149
 
< 0.1%
1.922 × 10-149
 
< 0.1%
Other values (836050) 984782
98.5%
ValueCountFrequency (%)
-7.241425 × 10-121
< 0.1%
-7.0565975 × 10-121
< 0.1%
-6.741441 × 10-121
< 0.1%
-6.5239223 × 10-121
< 0.1%
-6.439141 × 10-121
< 0.1%
-6.4076746 × 10-121
< 0.1%
-6.3749289 × 10-121
< 0.1%
-6.30414298 × 10-121
< 0.1%
-6.279092 × 10-121
< 0.1%
-6.277296 × 10-121
< 0.1%
ValueCountFrequency (%)
8.884224 × 10-121
< 0.1%
7.170111 × 10-121
< 0.1%
7.04828 × 10-121
< 0.1%
6.994028 × 10-121
< 0.1%
6.896834 × 10-121
< 0.1%
6.6253107 × 10-121
< 0.1%
6.2697284 × 10-121
< 0.1%
6.0630616 × 10-121
< 0.1%
6.0403524 × 10-121
< 0.1%
5.892845 × 10-121
< 0.1%

Interactions

2023-02-24T19:00:35.804655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:20.788798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:22.754515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:24.629530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:26.460636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:28.315686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:30.130796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:32.062664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:33.959589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:36.008109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:21.017188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:22.955976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:24.827004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:26.670046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:28.522095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:30.342262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:32.277086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:34.163043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:36.215527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:21.246547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:23.167411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:25.029461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:26.878520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:28.729569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:30.564635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:32.514423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:34.367501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:36.409038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:21.469980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:23.372894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:25.218955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:27.072998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:28.924053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:30.772114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:32.719901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:34.569954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:36.629458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:21.692355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:23.576348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:25.423414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:27.271467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:29.114514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:30.985509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:32.924354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:34.775409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:36.827921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:21.899828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:23.779802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:25.620880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:27.471932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:29.305030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:31.187003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:33.121811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:34.973850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:37.049326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:22.117222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:23.999216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:25.834312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:27.691349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:29.519430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:31.407418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:33.331239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:35.188302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:37.253782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:22.327662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:24.200651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:26.031786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:27.897796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:29.725908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:31.623858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:33.536691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:35.389763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:37.451254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:22.537124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:24.412121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:26.246213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:28.102246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:29.923380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:31.841253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:33.741170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T19:00:35.595216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-24T19:00:43.029357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diffstormstorm phase
400kmDensity1.000-0.2990.3920.7700.8200.8470.8130.8550.0490.1630.116
SYM/H_INDEX_nT-0.2991.000-0.520-0.169-0.184-0.199-0.162-0.200-0.0050.1650.144
1-M_AE_nT0.392-0.5201.0000.3090.3280.3450.2860.3520.0080.2160.155
DAILY_SUNSPOT_NO_0.770-0.1690.3091.0000.9340.9050.8920.8930.0100.2200.161
DAILY_F10.7_0.820-0.1840.3280.9341.0000.9550.9380.9490.0100.0640.057
SOLAR_LYMAN-ALPHA_W/m^20.847-0.1990.3450.9050.9551.0000.9490.9920.0100.2350.171
mg_index (core to wing ratio (unitless))0.813-0.1620.2860.8920.9380.9491.0000.9410.0080.1990.144
irradiance (W/m^2/nm)0.855-0.2000.3520.8930.9490.9920.9411.0000.0100.2510.183
d_diff0.049-0.0050.0080.0100.0100.0100.0080.0101.0000.0540.038
storm0.1630.1650.2160.2200.0640.2350.1990.2510.0541.0001.000
storm phase0.1160.1440.1550.1610.0570.1710.1440.1830.0381.0001.000
2023-02-24T19:00:43.203921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.3450.3090.7400.4520.8210.7930.8290.2060.2020.058
SYM/H_INDEX_nT-0.3451.000-0.548-0.186-0.125-0.215-0.182-0.212-0.294-0.344-0.003
1-M_AE_nT0.309-0.5481.0000.2320.1390.2630.2150.2710.2910.2860.001
DAILY_SUNSPOT_NO_0.740-0.1860.2321.0000.5230.9050.8970.8860.1890.1790.003
DAILY_F10.7_0.452-0.1250.1390.5231.0000.5400.5300.5310.1390.1380.001
SOLAR_LYMAN-ALPHA_W/m^20.821-0.2150.2630.9050.5401.0000.9620.9890.2150.2060.003
mg_index (core to wing ratio (unitless))0.793-0.1820.2150.8970.5300.9621.0000.9490.1560.1490.002
irradiance (W/m^2/nm)0.829-0.2120.2710.8860.5310.9890.9491.0000.2230.2120.003
storm0.206-0.2940.2910.1890.1390.2150.1560.2231.0000.9670.001
storm phase0.202-0.3440.2860.1790.1380.2060.1490.2120.9671.0000.002
d_diff0.058-0.0030.0010.0030.0010.0030.0020.0030.0010.0021.000
2023-02-24T19:00:43.379455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.2990.3920.7700.8200.8470.8130.8550.2680.2600.049
SYM/H_INDEX_nT-0.2991.000-0.520-0.169-0.184-0.199-0.162-0.200-0.332-0.413-0.005
1-M_AE_nT0.392-0.5201.0000.3090.3280.3450.2860.3520.3210.3250.008
DAILY_SUNSPOT_NO_0.770-0.1690.3091.0000.9340.9050.8920.8930.2140.1950.010
DAILY_F10.7_0.820-0.1840.3280.9341.0000.9550.9380.9490.2290.2090.010
SOLAR_LYMAN-ALPHA_W/m^20.847-0.1990.3450.9050.9551.0000.9490.9920.2390.2210.010
mg_index (core to wing ratio (unitless))0.813-0.1620.2860.8920.9380.9491.0000.9410.1780.1610.008
irradiance (W/m^2/nm)0.855-0.2000.3520.8930.9490.9920.9411.0000.2440.2250.010
storm0.268-0.3320.3210.2140.2290.2390.1780.2441.0000.9450.005
storm phase0.260-0.4130.3250.1950.2090.2210.1610.2250.9451.0000.005
d_diff0.049-0.0050.0080.0100.0100.0100.0080.0100.0050.0051.000
2023-02-24T19:00:43.558969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.2060.2660.5760.6150.6460.6050.6560.2190.2010.035
SYM/H_INDEX_nT-0.2061.000-0.365-0.118-0.125-0.135-0.110-0.136-0.274-0.316-0.004
1-M_AE_nT0.266-0.3651.0000.2140.2230.2330.1920.2380.2630.2510.006
DAILY_SUNSPOT_NO_0.576-0.1180.2141.0000.7900.7380.7200.7210.1790.1560.007
DAILY_F10.7_0.615-0.1250.2230.7901.0000.8110.7780.8000.1870.1630.007
SOLAR_LYMAN-ALPHA_W/m^20.646-0.1350.2330.7380.8111.0000.8020.9260.1950.1720.007
mg_index (core to wing ratio (unitless))0.605-0.1100.1920.7200.7780.8021.0000.7820.1450.1260.005
irradiance (W/m^2/nm)0.656-0.1360.2380.7210.8000.9260.7821.0000.1990.1750.007
storm0.219-0.2740.2630.1790.1870.1950.1450.1991.0000.8980.004
storm phase0.201-0.3160.2510.1560.1630.1720.1260.1750.8981.0000.004
d_diff0.035-0.0040.0060.0070.0070.0070.0050.0070.0040.0041.000
2023-02-24T19:00:43.739486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.0000.5700.2600.6320.5140.6990.6810.7050.2130.1920.267
SYM/H_INDEX_nT0.5701.0000.4400.2850.2620.2020.1950.2020.2150.2340.170
1-M_AE_nT0.2600.4401.0000.2220.1380.2190.2070.2200.2820.2520.131
DAILY_SUNSPOT_NO_0.6320.2850.2221.0000.6880.8810.8560.8500.2870.2610.122
DAILY_F10.7_0.5140.2620.1380.6881.0000.6630.6280.6200.0960.0600.083
SOLAR_LYMAN-ALPHA_W/m^20.6990.2020.2190.8810.6631.0000.9520.9720.3060.2750.140
mg_index (core to wing ratio (unitless))0.6810.1950.2070.8560.6280.9521.0000.9230.2600.2360.122
irradiance (W/m^2/nm)0.7050.2020.2200.8500.6200.9720.9231.0000.3270.2930.141
storm0.2130.2150.2820.2870.0960.3060.2600.3271.0001.0000.070
storm phase0.1920.2340.2520.2610.0600.2750.2360.2931.0001.0000.064
d_diff0.2670.1700.1310.1220.0830.1400.1220.1410.0700.0641.000
2023-02-24T19:00:43.894073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
stormstorm phase
storm1.0001.000
storm phase1.0001.000

Missing values

2023-02-24T19:00:37.628777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-24T19:00:38.131405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
10426369.783691e-138.0104.018.083.80.0063780.2657600.00513011-7.377590e-14
23282183.695517e-1314.090.013.070.90.0064610.2646000.00524611-1.302370e-14
5780762.030248e-12-6.044.056.0104.60.0070560.2698900.005738111.958480e-13
5531842.223924e-12-9.0153.051.094.80.0072130.2690000.00573811-1.001860e-13
16405526.336876e-12-8.0279.0191.0159.70.0085430.2773700.006733111.055640e-13
1551521.821567e-13-1.06.00.071.10.0059990.2644990.004934112.236150e-14
15593666.414709e-12-63.0368.071.0123.00.0075890.2718600.006538113.148940e-13
35358555.789215e-13-14.09.011.066.20.0060650.2643930.00495112-1.539920e-14
7496171.963698e-12-3.0362.065.0104.80.0072270.2700800.005777111.083300e-14
5824922.222577e-12-11.0380.061.0102.40.0070430.2690800.005689127.883710e-13
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
9562091.609940e-13-6.040.00.068.10.0059540.2641210.004877-1-1-1.597580e-14
31888324.319766e-13-2.018.00.067.00.0060930.2641100.004962-1-1-3.285600e-15
3830639.469674e-136.0148.097.0105.90.0070650.2697600.005629-1-1-2.240310e-14
1013157.949391e-131.019.00.067.70.0060070.2635100.004970-1-12.958700e-15
30242721.352830e-120.081.029.081.90.0068110.2680600.005489-1-1-1.864280e-13
29001831.776567e-1213.059.087.0123.70.0072930.2738320.005775-1-1-6.539500e-14
6880531.030874e-135.024.00.068.40.0059520.2639520.004898-1-1-7.825600e-15
25380951.317815e-122.017.028.085.90.0066480.2687570.005350-1-1-8.136000e-15
29756003.214464e-1321.062.00.067.40.0061330.2638500.004984-1-1-3.968670e-14
1791733.281095e-12-9.087.0151.0157.80.0081920.2752500.006818-1-1-1.609610e-13

Duplicate rows

Most frequently occurring

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff# duplicates
02.814039e-13-8.030.00.066.60.006020.2644570.004949-1-10.02